Question

我目前正试图在matlab中绘制一个风玫瑰图，其中包含给定时期内的数据风速和方向。主要程序是在绘制了Weibull分布上的几个图之后，它调用了另一个matlab程序来产生风玫瑰。

风玫瑰计划基本上就在这里：https://www.mathworks.com/matlabcentral/fileexchange/47248-wind-rose

主程序主要基于https://www.mathworks.com/matlabcentral/fileexchange/41996-computing-weibull-distribution-parameters-from-a-wind-speed-time-series?focused=3786165&tab=function

我昨天在一个非常老的matlab版本上工作，我在编写代码时遇到了严重的问题。今天使用Octave在Ubuntu机器上，经过一些努力，我设法得到一个小问题的结果：风玫瑰没有它应该拥有的所有信息。

我在新版本的matlab中运行该程序，然后收到以下消息：

Error using WindRose (line 244)
 is not a valid property for WindRose function.
Error in octavetestoforiginalprogram (line 184)
[figure_handle,count,speeds,directions,Table] = WindRose(dir,vel,Options);

该程序如何在Octave中运行，现在会产生这样的错误，我不明白。

有没有人知道这个错误意味着什么？

注意：如果有人想阅读，我会在下面发布完整的代码：

%% EXTRACT AND PLOT RAW DATA

% Extract wind speed data from a file
v = xlsread('1981-1985_timeseries.xlsx');

% Plot the measured wind speed
plot(v)
title('Wind speed time series');
xlabel('Measurement #');
ylabel('Wind speed [m/s]');





%% PROCESS DATA

% Remove nil speed data (to avoid infeasible solutions in the following)
v(find(v==0)) = [];
% Extract the unique values occuring in the series
uniqueVals = unique(v);
uniqueVals(isnan(uniqueVals))=[];

% Get the number of unique values
nbUniqueVals = length(uniqueVals);

% Find the number of occurences of each unique wind speed value
for i=1:nbUniqueVals
    nbOcc = v(find(v==uniqueVals(i)));
    N(i) = length(nbOcc);
end

% Get the total number of measurements
nbMeas = sum(N);

% To take into account the measurement resolution
% (i.e., a measured wind speed of 2.0 m/s may actually correspond to a
% real wind speed of 2.05 or 1.98 m/s), compute the delta vector which
% contains the difference between two consecutive unique values
delta(1) = uniqueVals(1);
for i=2:nbUniqueVals
    delta(i) = uniqueVals(i) - uniqueVals(i-1);
end

% Get the frequency of occurence of each unique value
for i=1:nbUniqueVals
    prob(i) = N(i)/(nbMeas*delta(i));
end



% Get the cumulated frequency
freq = 0;
for i=1:nbUniqueVals
    freq = prob(i)*delta(i) + freq;
    cumFreq(i) = freq;
end


%% PLOT THE RESULTING DISTRIBUTION

% Plot the distribution
figure
subplot(2,1,1);
pp=plot(uniqueVals,prob)
title('Distribution extracted from the time series');
xlabel('Wind speed [m/s]');
ylabel('Probability');

% Plot the cumulative distribution
subplot(2,1,2);
plot(uniqueVals,cumFreq)
title('Cumulative distribution extracted from the time series');
xlabel('Wind speed [m/s]');
ylabel('Cumulative probability');


%% EXTRACT THE PARAMETERS USING A GRAPHICAL METHOD

% See the following references for more explanations:
% - Akdag, S.A. and Dinler, A., A new method to estimate Weibull parameters
% for wind energy applications, Energy Conversion and Management,
% 50 :7 1761�1766, 2009
% - Seguro, J.V. and Lambert, T.W., Modern estimation of the parameters of
% the Weibull wind speed distribution for wind energy analysis, Journal of
% Wind Engineering and Industrial Aerodynamics, 85 :1 75�84, 2000


% Linearize distributions (see papers)
ln = log(uniqueVals);
lnln = log(-log(1-cumFreq));

% Check wether the vectors contain inifinite values, if so, remove them
test = isinf(lnln);
for i=1:nbUniqueVals
    if (test(i)==1)
        ln(i)= [];
        lnln(i)= [];
    end
end

% Extract the line parameters (y=ax+b) using the polyfit function
params = polyfit(ln,lnln',1);
a = params(1);
b = params(2);
y=a*ln+b;

% Compare the linealized curve and its fitted line
figure
plot(ln,y,'b',ln,lnln,'r')
title('Linearized curve and fitted line comparison');
xlabel('x = ln(v)');
ylabel('y = ln(-ln(1-cumFreq(v)))');

% Extract the Weibull parameters c and k
k = a
c = exp(-b/a)


%% CHECK RESULTS

% Define the cumulative Weibull probability density function
% F(V) = 1-exp(-((v/c)^k)) = 1-exp(-a2), with a1 = v/c, a2 = (v/c)^k
a1 = uniqueVals/c;
a2 = a1.^k;
cumDensityFunc = 1-exp(-a2); 

% Define the Weibull probability density function
%f(v)=k/c*(v/c)^(k-1)*exp(-((v/c)^k))=k2*a3.*exp(-a2), 
% with  k2 = k/c, a3 = (v/c)^(k-1)
k1 = k-1;
a3 = a1.^k1;
k2 = k/c;
densityFunc = k2*a3.*exp(-a2);  

% Plot and compare the obtained Weibull distribution with the frequency plot
figure
subplot(2,2,1);
pp=plot(uniqueVals,prob,'.',uniqueVals,densityFunc, 'r')
title('Weibull probability density function');
xlabel('v');
ylabel('f(v)');

subplot(2,2,3)
h=hist(v);
title('Wind speed time series');
xlabel('Measurement #');
ylabel('Wind speed [m/s]');
h=h/(sum(h)*10);
bar(h)

% Same for the cumulative distribution
subplot(2,2,2);
plot(uniqueVals,cumFreq,'.',uniqueVals,cumDensityFunc, 'r')
title('Cumulative Weibull probability density function');
xlabel('v');
ylabel('F(V)');


%inner
figure
hold on
pp=plot(uniqueVals,prob,'.',uniqueVals,densityFunc, 'r')
title('Weibull probability density function');
xlabel('v');
ylabel('f(v)');

bar(h)

hold off

%inner



%rose

w=xlsread('rose.xlsx');
dir=w(:,2)*10;
vel=w(:,1);

Options = {'anglenorth','FreqLabelAngle',0,'angleeast','FreqLabelAngle',90,'labels',{'N (0)','S (180)','E (90)','W (270)'},'freqlabelangle',45,'nDirections',20,'nFreq',25,'LegendType',1};
[figure_handle,count,speeds,directions,Table] = WindRose(dir,vel,Options);

close all; clear Options;

在matlab的风玫瑰图情节

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